WO2022016862A1 - 用于监控电池组健康状况的方法和系统 - Google Patents

用于监控电池组健康状况的方法和系统 Download PDF

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WO2022016862A1
WO2022016862A1 PCT/CN2021/077167 CN2021077167W WO2022016862A1 WO 2022016862 A1 WO2022016862 A1 WO 2022016862A1 CN 2021077167 W CN2021077167 W CN 2021077167W WO 2022016862 A1 WO2022016862 A1 WO 2022016862A1
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voltage difference
cells
battery pack
alarm value
electric vehicle
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PCT/CN2021/077167
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English (en)
French (fr)
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张轶珍
许永刚
瑞吉尔·托纳蒂乌
祁宏钟
尚进
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广州汽车集团股份有限公司
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Priority to CN202180004068.5A priority Critical patent/CN114631032A/zh
Publication of WO2022016862A1 publication Critical patent/WO2022016862A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/3644—Constructional arrangements
    • G01R31/3646—Constructional arrangements for indicating electrical conditions or variables, e.g. visual or audible indicators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery

Definitions

  • the present disclosure relates to the technical field of electric vehicles, and in particular, to a method and system for monitoring the health status of a battery pack.
  • New energy vehicles include electric vehicles (EVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).
  • EVs electric vehicles
  • HEVs hybrid electric vehicles
  • PHEVs plug-in hybrid electric vehicles
  • New energy vehicles can transmit real-time vehicle data to an Internet cloud server for remote monitoring and data collection.
  • the new energy vehicle model has accumulated a lot of data over time. The data hides precious clues about the performance and health of NEVs, especially for battery packs, which are a key component of NEVs.
  • the embodiments of the present application provide a method and system for monitoring the health status of a battery pack, aiming to solve the problem of how to establish an effective method to monitor the health status of a battery pack of a new energy vehicle.
  • a method for monitoring the health status of a battery pack comprising: obtaining a voltage difference between a maximum voltage and a minimum voltage between cells in a battery pack of an electric vehicle; The voltage difference value determines an alarm value, wherein the alarm value is a composite value of the following factors: the slope of the average voltage difference of the cells in the past preset time period, the predicted average voltage difference of the cells in the future preset time period, and the cell The minimum voltage difference value; when the alarm value is greater than the threshold value, a predictive maintenance notification is generated for the electric vehicle battery pack.
  • the method before acquiring the voltage difference between the maximum voltage and the minimum voltage between the cells in the battery pack of the electric vehicle, the method further includes: reporting the electric vehicle through an onboard sensor and/or a CAN bus of the electric vehicle. Voltage-related data for individual cells within a vehicle battery pack.
  • the calculating an alarm value based on the voltage difference value includes: analyzing a time series of voltage-related data of each cell in the battery pack through a cloud-based server or an in-vehicle computing device to obtain an alarm value.
  • the alarm value is a weighted average of the slope of the average voltage difference of the cells, the predicted average voltage difference of the cells in the future preset time period, and the minimum voltage difference of the cells. value.
  • the calculation formula of the alarm value L d in a given time is as follows:
  • L 1 , L 2 , and L 3 respectively represent the slope of the average voltage difference of the cells, the predicted average voltage difference of the cells in the future preset time period, and the minimum voltage difference of the cells; W 1 , W 2 , and W 3 are non-negative weight coefficients of L 1 , L 2 , and L 3 , respectively.
  • W 1 , W 2 , W 3 are determined according to the type of battery pack.
  • the method further includes: obtaining a current alarm value based on the alarm value L d , wherein the current alarm value is a weighted average of alarm values within a number of preset time periods.
  • the current alarm value calculation formula L p as follows:
  • N represents the number of sampling periods included in the backtracking window
  • w n represents the weight coefficient of the Nth sampling period
  • the method further includes optimizing the threshold based on the electric vehicle battery pack currently having error reports and the electric vehicle battery pack being currently free of error reports.
  • the method further includes sending the predictive maintenance notification to a designated terminal.
  • a system for monitoring battery pack health includes: an acquisition module for acquiring a voltage difference between the maximum voltage and the minimum voltage between cells in an electric vehicle battery pack; a calculation module for calculating an alarm value based on the voltage difference, wherein the alarm value is the comprehensive value of the following factors: the slope of the average voltage difference of the cells in the past preset time period, the predicted average voltage difference of the cells in the future preset time period, and the minimum voltage difference of the cells; the generation module is used when When the alarm value is greater than a threshold, a predictive maintenance notification is generated for the electric vehicle battery pack.
  • the alarm value is a weighted average of the slope of the average voltage difference of the cells, the predicted average voltage difference of the cells in the future preset time period, and the minimum voltage difference of the cells. value.
  • the calculation formula of the alarm value L d in a given time is as follows:
  • L 1 , L 2 , and L 3 respectively represent the slope of the average voltage difference of the cells, the predicted average voltage difference of the cells in the future preset time period, and the minimum voltage difference of the cells; W 1 , W 2 , and W 3 are non-negative weight coefficients of L 1 , L 2 , and L 3 , respectively.
  • W 1 , W 2 , W 3 are determined according to the type of battery pack.
  • the obtaining module further includes: obtaining a current alarm value based on the alarm value L d , wherein the current alarm value is a weighted average of alarm values within a number of preset time periods.
  • the current alarm value calculation formula L p as follows:
  • N represents the number of sampling periods included in the backtracking window
  • w n represents the weight coefficient of the Nth sampling period
  • system further includes an optimization module for optimizing the threshold value based on the electric vehicle battery pack currently having errors reported and the electric vehicle battery pack being currently free of errors.
  • system further includes: a sending module configured to send the predictive maintenance notification to a designated terminal.
  • a non-volatile computer-readable storage medium stores a program, and when the program is executed by a computer, the above-mentioned embodiments are implemented. method steps.
  • an electric vehicle includes the system of the above-described embodiments for monitoring the health of the battery pack.
  • the alarm value is obtained by analyzing the relevant voltage difference data of the battery pack. Based on the alarm values, various degradation trends of abnormal battery packs can be detected, providing early warning to OEMs, dealers and end customers. Further, dealers can perform troubleshooting and predictive maintenance as quickly as needed to extend battery life and reduce warranty costs.
  • FIG. 1 is a flowchart of a method for monitoring the health of a battery pack provided according to an embodiment of the present application
  • FIG. 2 is a flowchart of a method for monitoring the health of a battery pack provided according to another embodiment of the present application
  • FIG. 3 is a schematic diagram illustrating the comparison of alarm distribution between an error reporting group and a non-error reporting group provided according to an embodiment of the present application
  • FIG. 5 is a structural block diagram of a system for monitoring the health status of a battery pack provided according to an embodiment of the present application
  • FIG. 6 is a structural block diagram of a system for monitoring the health of a battery pack provided according to another embodiment of the present application.
  • FIG. 7 is a structural block diagram of an electric vehicle provided according to an embodiment of the present application.
  • this embodiment provides a data-driven battery pack health monitoring method based on voltage-related data of each cell in the battery pack. As shown in Figure 1, the method includes the following steps.
  • Step S102 Obtain the voltage difference between the maximum voltage and the minimum voltage between the cells in the battery pack of the electric vehicle.
  • Step S104 Calculate an alarm value based on the voltage difference, wherein the alarm value is a comprehensive value of the following factors: the slope of the average voltage difference of the cells in the past preset time period, the predicted average voltage difference of the cells in the future preset time period and the minimum voltage difference of the cells.
  • Step S106 When the alarm value is greater than the threshold value, generate a predictive maintenance notification for the electric vehicle battery pack.
  • the method may further include the following steps: reporting voltage-related data of each cell in the battery pack of the electric vehicle through on-board sensors and/or CAN bus of the electric vehicle.
  • the battery voltage can be measured using voltage sensors connected to the individual cells. In this way, measurements can be taken while the vehicle is in use.
  • the reliability of the data measured by the voltage sensor can be further improved by measuring the battery voltage for one or more seconds in real time within a preset sampling period.
  • step S104 may further include the following step: by a cloud-based server or an on-board computing device, analyzing the time series of voltage-related data of each cell in the battery pack to obtain an alarm value.
  • the alarm value is a weighted average of the slope of the average voltage difference of the cells, the predicted average voltage difference of the cells within a preset time period in the future, and the minimum voltage difference of the cells.
  • the method further includes the step of: obtaining a current alarm value based on the alarm value, wherein the current alarm value is a weighted average of the alarm values in a preset several time periods.
  • the method further includes the step of optimizing the threshold based on the electric vehicle battery pack currently having errors reported and the electric vehicle battery pack being currently free of errors.
  • step S106 the method further includes the following step: sending a predictive maintenance notification to a designated terminal.
  • This embodiment provides an alarm model.
  • the alert model can be used to analyze relevant data stored in cloud servers and generate automatic notifications for predictive maintenance for battery packs of different types of NEVs (eg EV, HEV, PHEV, etc.) .
  • Battery life can be extended for a better user experience, and warranty costs can be significantly reduced if troubleshooting and predictive maintenance are performed immediately after early warning notification.
  • This application provides a method to achieve the highest level of predictive maintenance and user satisfaction without the poor user experience and high OEM warranty costs associated with reactive maintenance.
  • FIG. 2 shows a flowchart of an example of the present application. It should be noted that the method shown in Figure 2 is applicable to all types of new energy vehicles, and the plug-in hybrid electric vehicle (PHEV) here is just an example. As shown in Figure 2, the process includes the following steps.
  • step S202 based on the historical data analysis of the time series of the voltage of a single cell of a plug-in hybrid electric vehicle (which has reported multiple battery pack failures), the difference between the maximum voltage and the minimum voltage between the cells in the battery pack is determined.
  • the value is an important indicator for judging the health of the battery pack.
  • the daily alarm value L d can be determined according to the daily voltage difference statistics between the cells of each vehicle during driving, which is a comprehensive value of the following three factors.
  • the first factor is the slope of the daily average voltage difference between cells over a period of time.
  • the slope can be a time series trend of the daily average voltage difference between cells over the past 30 days.
  • the second factor is the predicted daily average voltage difference between cells in the future based on current cell-to-cell voltage differences and time-series trends. For example, the daily average voltage difference between the cells in the next 30 days can be predicted based on the current time series trend of the voltage difference between the cells and the voltage difference between the cells in the past 30 days.
  • the third factor is the daily minimum voltage difference between cells. Multiple daily voltage differences between cells can be measured and collected, from which the daily minimum voltage difference between cells can be selected.
  • “daily” is only an example, but not a limitation, and may be other time periods, such as every hour, every N hours, or every N days.
  • different threshold values can be defined for the above three factors according to the needs of different vehicle battery types, and the ratio between the actual value and the threshold value can be defined as different alarm factors, namely L 1 , L 2 , L 3 .
  • the daily alarm value L d can be determined by the weighted average of the above three alarm factors, and the calculation formula is as follows:
  • step S206 for each vehicle, its current alarm value Lp may be determined by a normalized weighted average of the daily alarm values within a time period (eg, the last N days). For example, the last day has a weight of 1, and the weight decays exponentially in the days preceding the last day.
  • the current alarm value Lp can be determined according to the following formula:
  • step S208 all vehicles that have driven in the past N days can be ranked according to the current alarm value, and the alarm value higher than a certain threshold can be regarded as an emergency warning, and it is recommended that the dealer pay attention and maintain it immediately.
  • the alarm model results presented in this example are validated by battery pack error reporting.
  • the alarm value for the day the user reports the battery pack failure to the dealership can be calculated and compared to the current alarm value of all active vehicles.
  • Figure 3 is a schematic diagram showing the comparison of the distribution of alarm values between the groups with and without error reports. As shown in Figure 3, for one of the PHEVs studied, the distribution of alert values is significantly different for vehicles with and without false reports. For vehicles with error reports, the median alert value is about 1. For vehicles with no error reports, the median alert value is about 0.25.
  • 0.5 can be defined as the threshold between the normal alarm group and the abnormal alarm group, and the corresponding alarm model has a true positive rate of 83%, a false negative rate of 17%, and a false positive rate of 22%.
  • weights in formula (1) and formula (2) are trained and optimized. The same is true for the weights in the decay scheme formula (3).
  • parameters are selected to maximize the AUC value based on training data for vehicles with and without error reports.
  • the computer software product is stored in a storage medium (eg, read-only memory/random access memory, magnetic disk, and optical disk), including the software for enabling terminal equipment (which may be a mobile phone, computer, server, network device, etc.) to execute the present application Several instructions of the method in various embodiments.
  • a storage medium eg, read-only memory/random access memory, magnetic disk, and optical disk
  • This embodiment further provides a system for monitoring the health of a battery pack.
  • the system can be applied to cloud-based servers or in-vehicle computing devices for implementing the above-described embodiments in preferred implementations. It will not be repeated here.
  • the term "module” below may be a combination of software and/or hardware that implements a certain set function.
  • the devices described in the following embodiments are preferably implemented by software, and can also be implemented by hardware or a combination of software and hardware.
  • FIG. 5 is a structural block diagram of a system for monitoring the health status of a battery pack provided according to an embodiment of the present application. As shown in Figure 5, the system 100 includes:
  • the obtaining module 10 is configured to obtain the voltage difference between the maximum voltage and the minimum voltage between the cells in the battery pack of the electric vehicle.
  • the calculation module 20 is used to calculate an alarm value based on the voltage difference value, wherein the alarm value is a comprehensive value of the following factors: the slope of the average voltage difference of the battery cells in the past preset time period, the prediction of the battery cells in the future preset time period The average voltage difference and the minimum voltage difference of the cells.
  • a generation module 30 is configured to generate a predictive maintenance notification for the electric vehicle battery pack when the alarm value is greater than the threshold value.
  • FIG. 6 is another structural block diagram of a system for monitoring the health status of a battery pack provided according to an embodiment of the present application. As shown in Figure 6, the system also includes:
  • the optimization module 40 is configured to optimize the threshold based on the current error report of the electric vehicle battery pack and the current error report of the electric vehicle battery pack.
  • the sending module 50 is configured to send a predictive maintenance notification to a designated terminal.
  • the system can be implemented in a cloud-based server or in-vehicle computing device. It can analyze time series of relevant data provided by on-board sensors and/or CAN bus, identify all vehicles showing unhealthy degradation trends, and generate automatic forecasts for battery packs of different types of new energy vehicles (eg EV, HEV, PHEV, etc.) Sexual maintenance warning notice. It ensures that all active battery packs are operating within a healthy voltage differential range and detects any potential imbalance issues or unhealthy trends in battery packs at an early stage. Once a reasonable threshold for vehicle alerts has been determined, “Maintenance Required” warnings can be sent directly to OEMs, dealers and users in different ways for early troubleshooting and predictive maintenance to extend battery life, Reduce OEM warranty costs.
  • new energy vehicles eg EV, HEV, PHEV, etc.
  • a non-volatile computer-readable storage medium stores a program, and when the program is executed by a computer, the following steps are implemented.
  • Step S1 Obtain the voltage difference between the maximum voltage and the minimum voltage between the cells in the battery pack of the electric vehicle.
  • Step S2 Calculate an alarm value based on the voltage difference, wherein the alarm value is a comprehensive value of the following factors: the slope of the average voltage difference of the cells in the past preset time period, the predicted average voltage difference of the cells in the future preset time period and the minimum voltage difference of the cells.
  • Step S3 When the alarm value is greater than the threshold value, generate a predictive maintenance notification for the electric vehicle battery pack.
  • the storage medium includes, but is not limited to, various media capable of storing program codes, such as a USB flash drive, a read-only memory, a random access memory, a removable hard disk, a magnetic disk, or an optical disk.
  • program codes such as a USB flash drive, a read-only memory, a random access memory, a removable hard disk, a magnetic disk, or an optical disk.
  • an electric vehicle is provided.
  • the electric vehicle includes the system for monitoring the health of the battery pack in the above-described embodiments.
  • the electric vehicles in this embodiment may be different types of new energy vehicles (NEVs), such as electric vehicles (EV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and the like.
  • NEVs new energy vehicles
  • EV electric vehicles
  • HEV hybrid electric vehicles
  • PHEV plug-in hybrid electric vehicles
  • the system can analyze battery pack related data provided by on-board sensors and/or CAN bus, and identify all vehicles showing any unhealthy degradation trends, and generate automatic predictive maintenance warning notifications for NEV battery packs. It ensures that all active battery packs are operating within a healthy voltage differential range and detects any potential imbalance issues or unhealthy trends in battery packs at an early stage. Once reasonable vehicle alert thresholds have been identified, warnings can be sent directly to OEMs, dealers and users in different ways for early troubleshooting and predictive maintenance to extend battery life and reduce OEM costs Warranty cost.
  • each module or step of the present application can be executed by a general-purpose computer device, and these modules or steps can be centralized on a single computer device or distributed on a network formed by a plurality of computer devices, and in A certain embodiment may be implemented by program code executable by a computer device.
  • the module or step may be stored in a storage device for execution by a computer device.
  • steps shown or described may be performed in a different order than described herein, or may each form separate integrated circuit modules, or multiple modules or steps therein may form a single integrated circuit module for execution. Accordingly, the present application is not limited to any particular combination of hardware and software.

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Abstract

本申请提供了一种用于监控电池组健康状况的方法和系统。该方法包括:获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值；基于电压差值确定警报值，其中警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值；当警报值大于阈值时，为电动车辆电池组生成预测性维护通知。

Description

用于监控电池组健康状况的方法和系统 技术领域
本发明公开涉及电动车辆技术领域，特别涉及用于监控电池组健康状况的方法和系统。
背景技术
如今，随着人们对环境问题的日益关注，越来越多人开始接受新能源汽车(NEVs)。新能源汽车包括电动汽车(EVs)、混合动力汽车(HEVs)和插电式混合动力汽车(PHEVs)。新能源汽车可以将实时车辆数据传输到互联网云服务器实现远程监控和数据收集。因此，随着时间的推移，新能源汽车模型积累了大量数据。数据中隐藏着关于新能源汽车的性能和健康状况的珍贵线索，尤其是对于电池组，它是新能源汽车的关键组成部分。
可以根据这些有价值的数据建立有效的方法来监测新能源汽车电池组的健康状况。
需注意的是，本申请的背景技术中公开的内容仅用于增强对本申请的背景的理解，不是且不应被理解为是对本领域普通技术人员已知的现有技术的认可或任何形式的暗示。
发明内容
本申请的实施例提供了一种用于监控电池组健康状况的方法和系统，旨在解决如何建立有效的方法来监控新能源汽车电池组的健康状况问题。
根据本申请的一个实施例，提供了一种用于监控电池组健康状况的方法，该方法包括:获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值；基于所述电压差值确定警报值，其中所述警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值；当所述警报值大于阈值时，为电动车辆电池组生成预测性维护通知。
示例性实施例中，在获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值之前，该方法还包括:通过电动车辆的车载传感器和/或CAN总线，报告所述电动车辆电池组内的各个电芯的电压相关数据。
示例性实施例中，所述基于所述电压差值计算警报值包括:通过基于云的服务器或车载计算设备分析所述电池组内的各个电芯的电压相关数据的时间序列，获得警报值。
示例性实施例中，所述警报值是所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差以及所述电芯的最小电压差值的加权平均值。
示例性实施例中，给定时间内的所述警报值L d的计算公式如下:
L d＝W 1*L 1+W 2*L 2+W 3*L 3，W 1+W 2+W 3＝1；
其中，L 1、L 2、L 3分别表示所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差、所述电芯的最小电压差值；W 1、W 2、W 3分别是L 1、L 2、L 3的非负权重系数。
示例性实施例中，W 1、W 2、W 3根据电池组的类型来确定。
示例性实施例中，该方法还包括：基于所述警报值L d获取当前警报值，其中所述当前警报值是预设的若干个时间段内的警报值的加权平均值。
示例性实施例中，所述当前警报值L p的计算公式如下:
Figure PCTCN2021077167-appb-000001
其中，N表示回溯窗口包含的采样周期数，w n表示第N个采样周期的权重系数。
示例性实施例中，该方法还包括:基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化所述阈值。
示例性实施例中，在当所述警报值大于阈值时，为电动车辆电池组生成预测性维护通知之后，该方法还包括:向指定终端发送所述预测性维护通知。
根据本申请的另一实施例，提供了一种用于监控电池组健康状况的系统。该系统包括:获取模块，用于获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值；计算模块，用于基于所述电压差值计算警报值，其中所述警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值；生成模块，用于当所述警报值大于阈值时，为电动车辆电池组生成预测性维护通知。
示例性实施例中，所述警报值是所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差以及所述电芯的最小电压差值的加权平均值。
示例性实施例中，给定时间内的所述警报值L d的计算公式如下:
L d＝W 1*L 1+W 2*L 2+W 3*L 3，W 1+W 2+W 3＝1；
其中，L 1、L 2、L 3分别表示所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差、所述电芯的最小电压差值；W 1、W 2、W 3分别是L 1、L 2、L 3的非负权重系数。
示例性实施例中，W 1、W 2、W 3根据电池组的类型来确定。
示例性实施例中，所述获取模块还包括：基于所述警报值L d获取当前警报值，其中所述当前警报值是预设的若干个时间段内的警报值的加权平均值。
示例性实施例中，所述当前警报值L p的计算公式如下:
Figure PCTCN2021077167-appb-000002
其中，N表示回溯窗口包含的采样周期数，w n表示第N个采样周期的权重系数。
示例性实施例中，该系统还包括:优化模块，用于基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化所述阈值。
示例性实施例中，该系统还包括:发送模块，用于向指定终端发送所述预测性维护通知。
根据本申请的一个实施例，提供了一种非易失性计算机可读存储介质，所述非易失性计算机可读存储介质存储有程序，所述程序被计算机执行时实现上述实施例中的方法步骤。
根据本申请的一个实施例，提供了一种电动车辆。所述电动车辆包括上述实施例中的用于监控电池组健康状况的系统。
本申请的上述实施例，通过分析电池组的相关电压差数据，获得警报值。基于所述警报值，可以检测到异常电池组的各种退化趋势，为原始设备制造商、经销商和终端客户提供早 期预警。进一步，经销商可根据需要尽快进行故障排除和预测性维护，以延长电池寿命、降低保修成本。
附图说明
这里描述的附图为本申请提供了进一步理解，并且形成本申请的一部分。本申请的示意性实施例及其描述仅用于解释本申请，而非旨在限定本申请。
图1是根据本申请的一个实施例提供的用于监控电池组健康状况的方法的流程图；
图2是根据本申请的另一个实施例提供的用于监控电池组健康状况的方法的流程图；
图3是根据本申请的一个实施例提供的有错误报告组和无错误报告组之间的警报分布比较示意图；
图4是根据本申请的一个实施例提供的警报模型的ROC曲线；
图5是根据本申请的一个实施例提供的用于监控电池组健康状况的系统的结构框图；
图6是根据本申请的另一个实施例提供的用于监控电池组健康状况的系统的结构框图；
图7是根据本申请的一个实施例提供的电动车辆的结构框图。
具体实施方式
下面将参考附图并结合实施例详细描述本申请。应当理解，以下所说明的优选实施例仅用于说明和解释本发明，并不用于限定本发明。
实施例1
为建立有效的电池组健康监控方式，本实施例基于电池组内的各个电芯的电压相关数据，提供了一种数据驱动的电池组健康监控方法。如图1所示，该方法包括以下步骤。
步骤S102：获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值。
步骤S104：基于电压差值计算警报值，其中，警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值。
步骤S106：当警报值大于阈值时，为电动车辆电池组生成预测性维护通知。
经过以上步骤，分析电池组的相关电压差数据，获得警报值。基于警报值，可以检测到异常电池组的各种退化趋势，为原始设备制造商、经销商和终端客户提供早期预警。进一步，经销商可根据需要尽快进行故障排除和预测性维护，以延长电池寿命、降低保修成本。
示例性实施例中，在步骤S102之前，该方法可以进一步包括以下步骤:通过电动车辆的车载传感器和/或CAN总线，报告电动车辆电池组内的各个电芯的电压相关数据。
例如，可以使用连接到各个电芯的电压传感器来测量电池电压。这样，可以在车辆使用时进行测量。通过在预设的采样周期内实时测量一秒或多秒的电池电压，可以进一步提高电压传感器测量到的数据的可靠性。
示例性实施例中，步骤S104还可以包括以下步骤:通过基于云的服务器或车载计算设备，分析电池组内各个电芯的电压相关数据的时间序列，获得警报值。
示例性实施例中，警报值是电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值的加权平均值。
示例性实施例中，该方法还包括以下步骤：基于警报值获取当前警报值，其中当前警报值是预设的若干个时间段内的警报值的加权平均值。
示例性实施例中，该方法还包括以下步骤：基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化阈值。
示例性实施例中，在步骤S106之后，该方法还包括以下步骤:向指定终端发送预测性维护通知。
实施例2
本实施例提供了一种警报模型。所述警报模型可用于分析存储在云服务器中的相关数据，并为不同类型的新能源汽车(例如电动汽车、混合动力汽车、插电式混合动力汽车等)的电池组生成预测性维护自动通知。电池寿命可被延长，以带来更好的用户体验，且如果在发 出预警通知后立即进行故障排除和预测性维护，保修成本可以大大降低。本申请提供的方法可实现最高水平的预测性维护和用户满意度，不用像被动维护那样导致用户体验不佳，且原始设备制造商的保修费用高。
图2示出了本申请的一个示例的流程图。应当注意，图2所示的方法适用于所有类型的新能源汽车，这里的插电式混合动力汽车(PHEV)只是示例。如图2所示，该过程包括以下步骤。
步骤S202中，基于对一种插电式混合动力汽车(报告过多次电池组故障)的单个电芯电压的时间序列的历史数据分析，电池组内电芯间的最大电压和最小电压的差值是判定电池组健康状况的重要指标。
为使电池组正常工作，大多数情况下，需将电芯间的电压差控制在非常小的范围内。当电芯间的电压差持续增加时，某些车辆上会观察到电池组的异常退化，同时电池容量可能异常下降。事实表明，通过BMS软件更新和后续持续定期充电，可以纠正电池组的异常退化趋势。
步骤S204中，基于上述观察，可以根据行驶期间各个车辆的电芯间的每日电压差统计数据来确定每日警报值L d，其是以下三个因子的综合值。
第一个因子是某一时段内的电芯间的日平均电压差的斜率。比如，此斜率可以是过去30天内电芯间的日平均电压差的时间序列趋势。
第二个因子是基于当前电芯间电压差和时间序列趋势预测的未来电芯间日平均电压差。比如，可以基于当前电芯间电压差和过去30天的电芯间电压差的时间序列趋势来预测未来30天的电芯间的日平均电压差。
第三个因子是电芯间的每日最小电压差值。可测量并收集电芯间的多个每日电压差，从中选择电芯间的每日最小电压差值。
需注意，本实施例中，“每日”只是一个示例，而非限定，可以是其他时间段，例如每小时、每N个小时或者每N天等。
本实施例中，可根据不同车辆电池类型的需要，为上述三个因子定义不同的阈值，且实际值和阈值之间的比率可定义为不同的警报因子，即L 1、L 2、L 3。例如，每日警报值L d可由上述三个警报因子的加权平均值来确定，计算公式如下:
L d＝w 1*L 1+w 2*L 2+w 3*L 3      (1)
w 1+w 2+w 3＝1     (2)
步骤S206中，对于每种车辆，其当前警报值L p可由一个时间段内(例如，最后N天)的每日警报值的归一化加权平均值来确定。例如，最后一天的权重为1，权重在最后一天之前的几天内呈指数衰减。例如，当前警报值L p可以根据以下公式确定:
Figure PCTCN2021077167-appb-000003
步骤S208中，根据当前警报值可对过去N天内行驶过的所有车辆进行排名,高于特定阈值的警报值可认定为紧急警告，建议经销商立即关注和维护。
本实施例提供的警报模型结果是由电池组错误报告来验证的。可计算用户向经销商报告电池组故障当天的警报值，并与所有活跃中的车辆的当前警报值进行比较。
图3是有错误报告组和无错误报告组之间的警报值分布比较示意图。如图3所示，对于所研究的一种PHEV，有错误报告的车辆和无错误报告的车辆相比，警报值分布是显著不同的。对于有错误报告的车辆，警报值的中位数约为1。对于无错误报告的车辆，警报值的中位数约为0.25。
根据图3所示的结果，可将0.5界定为正常警报组和异常警报组间的阈值，相应的警报模型的真阳性率为83％，假阴性率为17％，假阳性率为22％。
当正常组和异常组的阈值不同时，得到不同的真阳性率以及假阳性率，获得的ROC曲线如图4所示。ROC曲线下面积(AUC)>0.8表示该警报模型有效。
本实施例中，对公式(1)和公式(2)中的权重进行了训练和优化。衰减方案公式(3)中的权重也是如此。在一实施例中，根据有错误报告和无错误报告的车辆的训练数据选择参数，使AUC值最大化。
通过对上述操作模式的描述，本领域技术人员可清楚地理解，实施例中的方法可结合软件和所需通用硬件平台来实现，当然，也可以通过硬件来实现。然而，很多情况下，前者是首选的实现方式。基于这种理解，本申请实质上对传统技术有益的技术方案可以以软件产品的形式来体现。该计算机软件产品存储在存储介质(如，只读存储器/随机存取存储器、磁盘 和光盘)中，包括用于使终端设备(可以是移动电话、计算机、服务器、网络设备等)执行本申请的各个实施例中的方法的若干指令。
实施例3
本实施例进一步提供了一种用于监控电池组健康状况的系统。该系统可应用于基于云的服务器或车载计算设备，用于以优选的实现方式来实现上述实施例。此处不再赘述。例如，下面的用语“模块”可以是实现某设定功能的软件和/或硬件的组合。以下实施例中描述的设备优选地由软件实现，也可以由硬件或软件和硬件的组合来实现。
图5是根据本申请的一个实施例提供的用于监控电池组健康状况的系统的结构框图。如图5所示，系统100包括:
获取模块10，用于获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值。
计算模块20，用于基于电压差值计算警报值，其中，警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值。
生成模块30，用于当警报值大于阈值时，为电动车辆电池组生成预测性维护通知。
图6是根据本申请的一实施例提供的用于监控电池组健康状况的系统的另一结构框图。如图6所示，该系统还包括:
优化模块40，用于基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化阈值。
发送模块50，用于向指定终端发送预测性维护通知。
本实施例中，该系统可以在基于云的服务器或车载计算设备中实现。它可以分析车载传感器和/或CAN总线提供的相关数据的时间序列，识别所有出现不健康退化趋势的车辆，并为不同类型的新能源汽车(如EV、HEV、PHEV等)的电池组生成自动预测性维护警告通知。它可确保所有活跃工作的电池组在健康的电压差范围内运行，并在早期检测出电池组潜在的任何失衡问题或者不健康的退化趋势。一旦合理的车辆警报阈值确定下来，就可以以不同的 方式将“需要维护”的警告直接发送给原始设备制造商、经销商和用户，以便尽早进行故障排除和预测性维护，从而延长电池寿命，降低原始设备制造商的保修成本。
实施例4
根据本实施例，提供了一种非易失性计算机可读存储介质，该非易失性计算机可读存储介质存储有程序，该程序被计算机执行时实现以下步骤。
步骤S1：获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值。
步骤S2：基于电压差值计算警报值，其中，警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值。
步骤S3：当警报值大于阈值时，为电动车辆电池组生成预测性维护通知。
在示例性实施例中，存储介质包括但不限于能够存储程序代码的各种介质，例如U盘、只读存储器、随机存取存储器、移动硬盘、磁盘或光盘。
实施例5
根据本实施例，提供了一种电动车辆。如图7所示，该电动车辆包括上述实施例中的用于监控电池组健康状况的系统。应当注意，本实施例中的电动车辆可以是不同类型的新能源汽车(NEVs)，例如电动汽车(EV)、混合动力汽车(HEV)、插入式混合动力汽车(PHEV)等。
本实施例中，该系统可以分析由车载传感器和/或CAN总线提供的电池组的相关数据，并且识别出现任何不健康退化趋势的所有车辆，为新能源汽车电池组生成自动预测维护警告通知。它可确保所有活跃工作的电池组在健康的电压差范围内运行，并在早期检测出电池组潜在的任何失衡问题或者不健康的退化趋势。一旦合理的车辆警报阈值确定下来，就可以以不同的方式直接向原始设备制造商、经销商和用户发送警告，以便尽早进行故障排除和预测性维护，从而延长电池寿命，降低原始设备制造商的保修成本。
显然，本领域技术人员应当知道，本申请的各个模块或步骤可以由通用计算机设备来执行，这些模块或步骤可以集中在单个计算机设备上或者分布在由多个计算机设备形成的网络上，并且在某一实施例中可以由计算机设备可执行的程序代码来实现。因此，该模块或步 骤可以存储在存储设备中，供计算机设备执行。某些情况下，所示出或描述的步骤可以以不同于这里描述的顺序执行，或者可以各自形成单独的集成电路模块，或者其中的多个模块或步骤可以形成供执行的单个集成电路模块。因此，本申请不限于任何特定的硬件和软件组合。
以上仅是本申请的示例性实施例，并不旨在限制本申请。对于本领域技术人员来说，本申请可以有多种改动和变化。凡按照本申请的精神和原理所做的修改、等同替换、改进等都应当理解为落入本申请的保护范围。

Claims (20)

  1. 一种用于监控电池组健康状况的方法，其特征在于，包括:
    获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值；
    基于所述电压差值计算警报值，其中，所述警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值；
    当所述警报值大于阈值时，为所述电动车辆电池组生成预测性维护通知。
  2. 根据权利要求1所述的方法，在获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值之前，还包括:
    通过电动车辆的车载传感器和/或CAN总线，报告所述电动车辆电池组内的各个电芯的电压相关数据。
  3. 根据权利要求1所述的方法，其特征在于，所述基于所述电压差值计算警报值包括:
    通过基于云的服务器或车载计算设备分析所述电池组内的各个电芯的电压相关数据的时间序列，获得警报值。
  4. 根据权利要求1所述的方法，其特征在于，所述警报值是所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差以及所述电芯的最小电压差值的加权平均值。
  5. 根据权利要求4所述的方法，其特征在于，给定时间内的所述警报值L d的计算公式如下:
    L d＝W 1*L 1+W 2*L 2+W 3*L 3，W 1+W 2+W 3＝1；
    其中，L 1、L 2、L 3分别表示所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差、所述电芯的最小电压差值；W 1、W 2、W 3分别是L 1、L 2、L 3的非负权重系数。
  6. 根据权利要求5所述的方法，其特征在于，权重系数W 1、W 2、W 3是根据所述电池组的类型来确定的。
  7. 根据权利要求1所述的方法，其特征在于，还包括:
    基于所述警报值L d获得当前警报值，其中，所述当前警报值是预设的若干个时间段内的警报值的加权平均值。
  8. 根据权利要求7所述的方法，其特征在于，所述当前警报值L p的计算公式如下:
    Figure PCTCN2021077167-appb-100001
    其中，N表示回溯窗口包含的采样周期数，w n表示第N个采样周期的权重系数。
  9. 根据权利要求1所述的方法，其特征在于，还包括:
    基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化所述阈值。
  10. 根据权利要求1所述的方法，其特征在于，还包括，在当所述警报值大于阈值时，为所述电动车辆电池组生成预测性维护通知的步骤之后，
    向指定的终端发送所述预测性维护通知。
  11. 一种用于监控电池组健康状况的系统，包括:
    获取模块，用于获取电动车辆电池组内的电芯间的最大电压和最小电压的电压差值；
    计算模块，用于基于所述电压差值计算警报值，其中，所述警报值是以下因子的综合值:过去预设时间段内电芯的平均电压差的斜率、未来预设时间段内电芯的预测平均电压差以及电芯的最小电压差值；
    生成模块，用于当所述警报值大于阈值时，为所述电动车辆电池组生成预测性维护通知。
  12. 根据权利要求11所述的系统，其特征在于，所述警报值是所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差以及所述电芯的最小电压差值的加权平均值。
  13. 根据权利要求12所述的系统，其特征在于，给定时间内的所述警报值L d的计算公式如下:
    L d＝W 1*L 1+W 2*L 2+W 3*L 3，W 1+W 2+W 3＝1；
    其中，L 1、L 2、L 3分别表示所述电芯的平均电压差的斜率、所述未来预设时间段内电芯的预测平均电压差、所述电芯的最小电压差值；W 1、W 2、W 3分别是L 1、L 2、L 3的非负权重系数。
  14. 根据权利要求13所述的系统，其特征在于，权重系数W 1、W 2、W 3是根据所述电池组的类型来确定的。
  15. 根据权利要求11所述的系统，其特征在于，所述获取模块还用于:
    基于所述警报值L d获得当前警报值，其中，所述当前警报值是预设的若干个时间段内的警报值的加权平均值。
  16. 根据权利要求15所述的系统，其特征在于，所述当前警报值L p的计算公式如下:
    Figure PCTCN2021077167-appb-100002
    其中，N表示回溯窗口包含的采样周期数，w n表示第N个采样周期的权重系数。
  17. 根据权利要求11所述的系统，其特征在于，还包括:
    优化模块，用于基于电动车辆电池组当前有错误报告和电动车辆电池组当前无错误报告，优化所述阈值。
  18. 根据权利要求11所述的系统，其特征在于，还包括:
    发送模块，用于向指定终端发送所述预测性维护通知。
  19. 一种非易失性计算机可读存储介质，其特征在于，所述非易失性计算机可读存储介质存储有程序，所述程序由计算机执行时实现如权利要求1所述的方法。
  20. 一种电动车辆，其特征在于，包括如权利要求11所述的系统。
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